使用dplyr排除当前观察值，计算组平均值 [英] Calculate group mean while excluding current observation using dplyr

问题描述

使用 dplyr （最好），我试图计算每次观察的组平均值，而不包括该组的观察值。

Using dplyr (preferably), I am trying to calculate the group mean for each observation while excluding that observation from the group.

似乎这应该是可以与 rowwise（）和 group_by的组合（），但两个函数都不能同时使用。

It seems that this should be doable with a combination of rowwise() and group_by(), but both functions cannot be used simultaneously.

给定这个数据框：

df <- data_frame(grouping = rep(LETTERS[1:5], 3),
                 value = 1:15) %>%
  arrange(grouping)
df
#> Source: local data frame [15 x 2]
#> 
#>    grouping value
#>       (chr) (int)
#> 1         A     1
#> 2         A     6
#> 3         A    11
#> 4         B     2
#> 5         B     7
#> 6         B    12
#> 7         C     3
#> 8         C     8
#> 9         C    13
#> 10        D     4
#> 11        D     9
#> 12        D    14
#> 13        E     5
#> 14        E    10
#> 15        E    15

我想让每个观察结果的组意味着排除在导致：

I'd like to get the group mean for each observation with that observation excluded from the group, resulting in:

#>    grouping value special_mean
#>       (chr) (int)
#> 1         A     1          8.5  # i.e. (6 + 11) / 2
#> 2         A     6            6  # i.e. (1 + 11) / 2
#> 3         A    11          3.5  # i.e. (1 + 6) / 2
#> 4         B     2          9.5
#> 5         B     7            7
#> 6         B    12          4.5
#> 7         C     3          ...

我尝试嵌套 rowwise（）在 do（）调用的函数内，但没有得到它的工作，沿着这些行：

I've attempted nesting rowwise() inside a function called by do(), but haven't gotten it to work, along these lines:

special_avg <- function(chunk) {
  chunk %>%
    rowwise() #%>%
    # filter or something...?
}

df %>%
  group_by(grouping) %>%
  do(special_avg(.))

推荐答案

不需要定义一个自定义函数，而是可以简单地求和组中的所有元素，减去当前值，除以每组元素数减去 1 。

No need to define a custom function, instead we could simply sum all elements of the group, subtract the current value, and divide by number of elements per group minus 1.

df %>% group_by(grouping) %>%
        mutate(special_mean = (sum(value) - value)/(n()-1))
#   grouping value special_mean
#      (chr) (int)        (dbl)
#1         A     1          8.5
#2         A     6          6.0
#3         A    11          3.5
#4         B     2          9.5
#5         B     7          7.0

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